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Data-Driven Risk Analyst
1 week ago
The SEB group is seeking a Data-Driven Risk Analyst to join their Financial Crime Prevention function. As a key member of the team, you will be responsible for analyzing large amounts of data to identify financial crime trends and emerging themes.
You will work closely with experienced colleagues to develop and manage contextual knowledge on banking sectors and specific financial crime risks associated with them. This information will be used to enhance detection capabilities and identify potential control or risk issues.
Your responsibilities will include managing operational inputs, developing large bodies of contextual knowledge, and applying technical and regulatory knowledge to support decision-making.
In addition, you will work collaboratively with transaction monitoring, sanctions, and other financial crime functions to manage identified risks and exposures. You will also drive data science and analytics initiatives across multiple projects to build tactical and strategic controls.
To succeed in this role, you should have experience with intelligence-led initiatives, preferably in a multi-business and geographical environment. An understanding of various financial crime schemes and typologies, as well as geopolitical aspects supporting them, is essential.
A relevant academic degree in IT, Finance, Business, Law, or equivalent, along with working knowledge of structuring large amounts of data and analytical methodological approaches for financial crime prevention, is required.
You should also have a strong track record of developing, producing, and presenting data-intensive analysis that are of strategic and/or operational importance. Experience in working within financial crime prevention areas, such as AML, or fraud investigations, or within a relevant police or state authority, is highly valued.
Required Skills and Qualifications
- Relevant academic degree in IT, Finance, Business, Law, or equivalent
- Working knowledge of structuring large amounts of data and analytical methodological approaches for financial crime prevention
- Strong track record of developing, producing, and presenting data-intensive analysis that are of strategic and/or operational importance
- Experience in working within financial crime prevention areas, such as AML, or fraud investigations, or within a relevant police or state authority
